Word Aligner exposes an MCP server so AI agents can turn a phrase and its translation into a shareable word-alignment diagram. The server runs over Streamable HTTP at aligner.tinygods.dev/mcp with no authentication and a single tool, create_word_alignment. An agent translates and tokenizes the text, works out which words correspond, calls the tool, and gets back a URL plus a preview image.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.6/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined in its description.
The single tool name 'create_word_alignment' follows a clear verb_noun pattern, which is consistent and predictable.
The server has only one tool, which is below the typical 3-15 range. However, for its narrow scope of creating word alignment diagrams, this single tool feels reasonable and not excessive.
The server provides a comprehensive create operation with detailed tokenization options, but lacks any retrieval, update, or delete capabilities. This is a notable gap for a full lifecycle, but the tool fully addresses its stated purpose of generating a diagram.
create_word_alignmentCreate word alignment diagramACreate a shareable Word Aligner diagram that shows which words match across two or more stacked lines of text (a translation and its source, an interlinear gloss, IPA, etc.). Returns a URL that opens the interactive diagram, plus a preview image.
Use this when the user wants to translate a phrase and show word correspondences, align a translation with its source (including RTL scripts like Hebrew or Arabic), or build a Leipzig-style interlinear gloss.
Word indices are 0-based token positions. Tokenize each line the same way the tool does before assigning indices:
Whitespace always splits ("I have been going" -> I[0] have[1] been[2] going[3]).
The characters in settings.tokenSplitChars (default ".-|") also split and are then removed from the rendered text, so "go.PST.IPFV" becomes three tokens (go, PST, IPFV) and the dots disappear. For Leipzig glosses set tokenSplitChars to "-|" to keep the dots.
Punctuation stays attached by default ("Hello, world!" -> Hello,[0] world![1]).
In RTL lines, word 0 is the logically first word (rightmost on screen); index in reading order.
Each alignment is [lineA, wordA, lineB, wordB]; the two lines must be vertically adjacent (|lineA - lineB| = 1). To express many-to-one, list each target word as its own tuple. Tokens that share a connection group get the same color automatically.
| Name | Required | Description | Default |
|---|---|---|---|
| lines | Yes | Text lines, top to bottom. Each entry is a plain string or an object with per-line visual options. | |
| pairs | No | Per-pair controls for a specific adjacent line pair. | |
| settings | No | Global visual overrides. Unset fields inherit defaults. | |
| alignments | No | Word-alignment links as [lineA, wordA, lineB, wordB] (0-based indices, lines must be adjacent). |
| Name | Required | Description |
|---|---|---|
| url | Yes | The shareable diagram URL. Return this to the user exactly as received, character for character. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only (readOnlyHint=true) and not destructive. The description goes far beyond by detailing tokenization rules (whitespace, tokenSplitChars, punctuation attachment), RTL index order, alignment constraints (adjacent lines, many-to-one representation), and automatic color grouping. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy but justified by the tool's complexity. It front-loads the core purpose and return type, then dives into critical usage details. Every sentence serves a purpose (tokenization, RTL handling, alignment syntax). Minor trimming could be possible, but overall it is well-structured and not verbose for the depth of information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, nested objects, no sibling tools), the description is complete. It covers purpose, return type, tokenization, alignment rules, global settings, and per-line options. The presence of an output schema (URL + preview image) and full schema coverage means the description does not need to explain return values—it already does. No gaps noted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. However, the description adds significant value by explaining how to use parameters like tokenSplitChars for Leipzig glosses, the meaning of RTL index order, and the format of alignments. It also clarifies the default character for tokenMergeChar and the behavior of tokenSplitChars. This goes beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it creates a shareable Word Aligner diagram showing word matches across stacked lines of text. It lists specific use cases (translation, interlinear gloss, RTL) and explains the return value (URL + preview image). The tool has no siblings, so differentiation is not needed, but the purpose is specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly spells out when to use the tool: for translation word correspondences, source alignment (including RTL), and Leipzig-style glosses. It also provides tokenization instructions and clarifies how punctuation is handled. While it does not explicitly state when not to use, the context is clear enough given no sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
